To this end, we propose a Siamese architecture to compute the similarity between templates and signal embeddings. Siamese networks employ two identical sub-networks to compare the similarity of two input samples, and they have a long history in performing verification tasks [1]}. The weights of the two sub-networks are shared together, and hence, they are likely to predict similar featuremaps for similar input samples. They can be further compared together, usually by computing the squared differences between the individual pairs of features. However, drawing motivation to emulate the popular Cosine similarity distance metric used for this purpose, in addition to computing the squared differences, we also explore the efficacy of multiplying the individual pairs of features. We term this as Product Proximity between the features, as similar valued feature pairs will yield a high multiplication output, whereas feature pairs with dissimilar values will result in a low score. Our intuition is that the way squared difference is intended to model eucledian distance, Product Proximity of the features will represent correlation or Cosine similarity like metrics. In addition to working with them separately, we also merge them together and analyze them in fully connected layers (please refer to Fig. REF ). Mathematically,
\(\textit {Squared Difference}[i] = (v_i - u_i)^2, \forall i \in [0\dots d]\) 
\(\textit {Product Proximity}[i] = (v_i \times u_i), \forall i \in [0\dots d]\) 
\(\textit {Combined Metric}[i] = \textbf {concatenate}(\textit {Squared Difference}[i],\textit {Product Proximity}[i])\) 
